Course Details

This self-paced, online course is composed of 12 modules – grouped into 4 sections, plus a primer (module 1) and a guide to next steps (module 12)

The course contains a number of supplemental materials, guides, and worksheets available for download and future reference.


Participants should expect that the course will take 
12-16 hours
to complete.

Part 1: How and why AI will transform the future of work

  • Module #2

  • Module #3

  • Module #4

AI’s workplace applications: an intuitive introduction

This module provides students with an intuitive introduction to AI applications in the workplace. It describes how, stripped down to their core, different AI applications can be framed as essentially doing the same thing: running data through a prediction engine in order to perform predefined tasks. It provides an accessible overview of potential inputs, how the prediction engine is built, and potential outputs, with reference to real-world examples.

The module then looks at AI applications through a different lens: the new capabilities they bring to the table - including automation of more complex tasks, deeper pattern detection, and inexpensive personalisation - and the business needs they can support. It ends by looking at the limitations of AI applications in the workplace, emphasizing that these technologies should not be thought of as a solution to every problem.

Part 2: Artificial intelligence and machine learning: what do you need to know?

  • Module #5

  • Module #6

The big picture: thinking clearly about AI, machine learning, and data science

The module will help participants clarify and declutter the terminology around the inter-related domains of artificial intelligence, machine learning, and data science. It equips participants with the foundational understanding of these domains that will be necessary for them to progress through the rest of the course. It aims to give participants confidence that while each of these domains can be extremely complex, gaining a cogent understanding of the big picture is well within their grasp.

It briefly looks at how the field of AI has evolved over the past few decades, touching upon why so many varied definitions of AI exist. It uses easily understandable examples to introduce the key differences between rule-based approaches to automation on the one hand, and statistical, ‘learning’-based approaches on the other. It ends with a discussion of how data science differs from more traditional data analytics methodologies.

Part 3: The anatomy of an AI project

  • Module #7

  • Module #8

Inside data science teams: who develops AI applications?

This module introduces participants to the specific roles and responsibilities of data scientists and other related technical specialists. Participants are introduced to the common educational and vocational backgrounds from which data scientists and other specialists typically come, the various types of experts commonly found on data teams, and the roles these individuals respectively play in the design, development, and implementation of AI and other data projects.

In examining these roles and backgrounds, students are introduced to common skill sets and domain expertise that data specialists often lack - thus highlighting areas where data specialists are likely to be particularly reliant on their business partners (such as the individuals taking this course) to help round out these gaps.

Part 4: How and why AI will transform the future of work

  • Module #9a

  • Module #9b

  • Module #10

  • Module #11

Partnering with data science teams: why they need you – and how you can help

This module helps line-of-business (LOB) managers, non-technical specialists, analysts, and other stakeholders to effectively and efficiently bring new AI applications into their organization.

The module provides a framework for participants to use when partnering with data teams, to define and set reasonable requirements, timelines, and expectations. It presents them with the tools to develop clearly-defined project plans, with measurable business outcomes - including how to properly define context, approach timelines and resource allocation.

Students will be introduced to the particular roles that they can (and should) play at each of the five key steps of AI projects: context setting, problem definition, preparing for development, development and testing, and project application. They will be shown how to best help data scientists and other specialists deliver solutions which provide maximum value to their organization. 

Take the first step in your journey toward fluency in AI, today  take the FIRST MODULE FREE

In Module 1, we tackle the ambiguity around AI, dispel common misconceptions, and help demystify what it means in the workplace.
We hope to convince you that you have an important role to play in your organization’s AI strategy.

Create a free account and start your journey today.

Ready to become fluent in AI?